Leveraging Advanced Analytics to Improve Tax Collection Performance

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NOTICE: Proprietary and ConfidentialThis material is proprietary to Opera Solutions. It contains trade secrets and confidential information which is solely the property of Opera Solutions. This material is solely for the Client’s internal use. This material shall not be used, reproduced, copied, disclosed, transmitted, in whole or in part, without the express consent of Opera Solutions.© 2011 Opera Solutions. All rights reserved.

Leveraging Advanced Analytics to Improve Tax Collection Performance

September 19, 2011

2

Introduction to Opera Solutions

The Opera Collections Diagnostic

The Delaware Recent Case Study

Opera Collector Workstation Demo

Opera Collections Insight Cube™ Demo

Questions

Agenda

3

Mission

OPERA’S MISSION

Drive significant, sustained profit growth by transforming raw Big Data flows into Signals and Actions through the

application of Machine Intelligence and Human Insight

4

Opera’s Unique Assets

P E O P L E D E L I V E R YS TA C K S O L U T I O N S

Scientists

Data Specialists

Business Domain Experts

Software Developers

Sales & Marketing Professionals

Hosted management/ delivery of solutions

Highly secure, flexible

Ongoing insertion directly to front lines of operations

Proprietary Signals and Analytics

Flexible, efficient, high-capacity, scalable

Global 250 – transformative Big Data solutions for Global 250

Vertical –

Creation and hosted delivery of solutions for specific business/functional problems

+ + +

5

Opera’s Unique Capabilities and Infrastructure

Analytics converted to

better business performance

Superior Signal Identification

“Lean” Delivery

Infrastructure

Superior Machine Learning Expertise

~160 scientists; 20+ disciplines

Adaptive learning platforms and models

Secure, flexible Supported by the

Insight Bureau

Signals Library Deep expertise in

variable transformation and selection

6

Vektor™ Scalable Big Data Analytics and Signals Processing Platform

Our Vektor™ platform allows for rapid implementation with low IT investment. It has the flexibility to address customers' requirements for both inbound data streams and

outbound "directed actions."

7

Opera’s Suite of Solutions

Premium Solutions Vertical SolutionsCreation and hosted delivery of Vektor-

platform-based solutions aimed at specific industry/functional problems

Opera Dynamic MarketingAuto Auction Pricing OptimizerAttrition ReductionTouch Curricula

Opera Waste, Fraud and AbuseFinancial Services FraudHealthcare W/F/ARevenue Leakage

Opera Spend Intelligence

BIQ Exploratory Analytics

Opera Performance AcceleratorFinancial Advisor PerformanceHospital PerformanceEducation PerformanceCollections Performance

Signals Hubs

Data Equity Assessment/Growth

Transformative Solutions

Working with Global 250 in financial services, government, healthcare,

and other selected sectors to transform their data reserves into

enterprise value

8

Acquisition/ Underwriting

Initial Line Assignment

Exposure Management

Pricing Modifications

Early Identification

Phone Treatment

Channel Management

Customer and Treatment Alignment

Treatment Optimization

Sales Practices Applied to Collections

Outside Agency Allocation

Legal ChannelsAccelerated

Handoffs

Proactive Rehabilitation

Post Write-offRecovery

InitialPricing

Collector Productivity

Rehabilitation Programs

Opera’s Experience Across the Collections Cycle

Account Opening Delinquency Charge-OffCurrent

Credit Allocation

Preemptive Intervention

Early Delinquency

Customer Contact

Treatment Offers

Resource Management

External Channels

Collections Insight Cube™

Collector’s Desktop

Enab

ling

Capa

biliti

esPa

st E

xper

ienc

e

9

More About Opera

– The State of New Jersey – The State of New York – The State of Delaware – The State of Illinois

– The State of Virginia – The City of New York– The City of Philadelphia– The Port Authority of New York and New Jersey

Opera just announced that it has obtained its first-ever outside equity funding: an $84 million minority investment

– This is one of the largest investments for a private Big Data predictive analytics firm to date

– Silver Lake Sumeru was the lead investor, with Accel-KKR also making a significant investment

In the recent Netfix Prize competition, Opera tied for first place (based on model performance), beating out 41,000 teams from over 180 countries

Opera was recently named Private Company of the Year by the New Jersey Technology Council, the region’s premier trade organization

Opera has been designated a Minority-Owned Firm by:

10






Questions

Agenda

11

Objectives and Approach

An Opera Collections Diagnostic identifies and prioritizes collections enhancement opportunities ensuring near term impact, and sustainable performance lift

Identify collections enhancement opportunities with meaningful impact and sustainable performance lift

6-8 week effort (assuming immediate availability of data) Review of book of business, existing processes and infrastructure Identification of collections enhancement opportunities through analytics and

operational improvements Quantification of financial impact and prioritization of the initiatives

Prioritized list of opportunities with associated financial impact Implementation blueprint to capture the identified opportunities Immediate kick-off of near term initiatives

O B J E C T I V E

A P P R O A C H

D E L I V E R A B L E S

12

Collections Diagnostic Process

Opera utilizes a proven methodology to uncover and prioritize opportunities across the collections spectrum

I. Establish Baseline

II. Identify Enhancement Opportunities

III. Develop Implementation

Blueprint

Book of business profile Collections operations map Infrastructure map

Collections enhancement map

Financial impact

Prioritized opportunities Implementation blueprint

F O C U S D E L I V E R A B L E S

Detailed view of book of business Collections processes and procedures Current /historical performance

Identify collections enhancement opportunities by – leveraging account-level data– Understanding collections

environment Assess financial impact

Prioritize opportunities based on:– Financial impact – Level of effort required

Develop implementation plans for each opportunity

13

Collections Diagnostic Benefits

The Opera Collections Diagnostic is a high impact, low risk opportunity

Revenue opportunities typically fall in the range if 15% to 20% of total

delinquencies for each client

– Opera has identified in excess of $1 billion combined improvement

opportunities

Minimal client capital and human resource investment is required

Opera often ties its compensation to results

14






Questions

Agenda

15

State of Delaware Case Study

Opera recently completed a Collections Diagnostic for the State of Delaware Department of Revenue

DOR management has graciously agreed to share the highlights of this study with today’s audience

All of us at Opera thank Delaware Director of Revenue Patrick Carter, Deputy Director of Revenue Colleen Yegla and Sharon Ferrara, Manager of the Bureau of Tax Collection for their collaboration and support during the course of this project

16

Portfolio Overview: Potential Opportunity

A significant portion of the Delaware portfolio that has never made a payment - $180MM. There is a substantial opportunity to improve collections performance for this group of accounts

$240 $42

$198 $180

$11$6

No Payment in 12 Months

Paid in Last 12 Months

Total Balances Never Paid Paid 12-24 Months

Last Payment >24 Months

DollarsCollected in Time Period

$17.9MM $10.4MM $3.3MM - $1.8MM $0.9MM

Inventory Overview by Payment HistoryAs of May 2011, Balances and Collections in Millions

Opera can help to capture an incremental 10 – 15% of this group

17

Account Distribution by Income Range

Debtors who make more than $50K make up only 34% of the total population, but they make up over 50% of the balances and payments; isolating these debtors may increase efficiencies in the tax portfolio

29%23%

17%

38%

28%30%

26%

34%35%

8%15% 18%

$50-100

>$100

$25-50

$0-25

Account Distribution by IncomeBalance in $ 000,000’s and Payments in $ 000’s

Average Balance

$3,085

$1,794

$1,058

$779

Average Payment

$9,256

$6,376

$3,631

$3,951

Total PaymentsOriginal BalanceAccounts

18

Balance, Collections, and Liquidation Heat Map

Segmenting the portfolio by income band and number of accounts per person shows that the low income, high account taxpayers perform much worse than the high income and low account debtors

1 Acct/Person 2 Accts/Person 3+ Accts/Person

AGI Incom

e

$0-25K $ 9,979,129 $ 6,526,763.39 $ 9,859,217.96

$25-50K $ 10,763,328 $ 7,586,545.02 $ 13,564,383.75

$50-100K $ 13,094,684 $ 8,603,574.52 $ 15,348,682.24

>$100K $ 11,880,942 $ 6,898,746.54 $ 11,358,012.65


AGI Income

$0-25K $ 2,534,319.00 $ 1,404,899.11 $ 1,134,723.89

$25-50K $ 3,247,573.07 $ 2,627,590.22 $ 3,194,226.55

$50-100K $ 4,033,071.58 $ 2,875,391.38 $ 3,549,837.07

>$100K $ 3,884,194.28 $ 2,070,179.92 $ 3,257,478.99


AGI Income

$0-25K 25% 22% 12%$25-50K 30% 35% 24%

$50-100K 31% 33% 23%>$100K 33% 30% 29%

Payments

DelinquentBalances

Liquidations

Most attractive segment consists of high income customers with one account

The lowest performing

segment consists of low income

individuals with multiple cases

19

Cumulative Payments Post-Placement by Income Band

After 18 months, the marginal increase in collections from one month to the next is very small for the lower income ranges

$ Collected by Income LevelCollections in $MM

1.0

3.0

5.0

0.0

6.56.0

4.0

2.0

3.5

1.5

0.5

4.5

5.5

2.5

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

11

10

9876543210

Months Since Placement

$ Collected, in MM

>100K

D. 25-50

50 - 100K

0 - 25K

20

Sample: Account Prioritization by Income Range

Debtors who make more than $50K make up only 34% of the total population, but they make up over 50% of the balances and payments; isolating these debtors may increase efficiencies in the tax portfolio

Account Distribution by Income1

Accounts in 0’s, Balance in $ 000,000 and Payments in $ 000’s

29%23%

17%

38%

28%30%

26%

34%35%

8%15% 18%

Total Payments

$0-25

$25-50

$50-100

>$100

Original Balance# of Accts

1 Income data from bc_case_history file and RETURN_AGI field from all accounts with AGI information

DOR can benefit from accelerated

outsourcing of accounts of taxpayers

earning less than $20k/year and

redirecting released resources to higher-

value accounts

21

Potential Strategy Changes

By shifting resources away from low income and low balance accounts, DOR can significantly increase the efforts against larger accounts, increasing collections in the more liquid high income segments

Work Effort Today

Work Effort Tomorrow¹

Potential Benefit²

Income Range

8% 20% $1.1M >$100K

20% 40% $2.2M $50 – 100K

30% 40% $1.0M $25 – 50K

40% - $0 $0 – 25K

Total Benefit $4.3M +13% 3 6 9 12 18

Months in Delinquency

Strategy

High Intensity for 18 Months – OCA in Mth

19 if No Payment

Medium Intensity for 18 Months – OCA in

Mth 19 if No Payment

Letters and Dialer Only for 9 Months – OCA in Mth 9 if No

Payment

Letters Only for 6 Months – OCA in Mth

7 if No Payment

Sample Strategies Using Income as a Differentiator

OCA

OCA

OCA

OCADialer

Work effort proportionate to % of total balances for taxpayers with income of$25+Benefit based on hypothetical 20% improvement based on reallocation of resources and improved automation; potential savings not included

ILLUSTRATIVE

22

Opera Recommendation Engine: How It Works

Opera will provide daily updates and prioritized lists of accounts to work to DOR collections management. Lists will be created based on Opera’s recommender models built using DOR data

Delaware Data Sources Opera Analytic Engine Opera Recommendations

Delaware Data Sets:

Delinquency DataIncome Data

Other Data Sources

Opera Recommendation Engine

Skip Trace Data: 3rd party

Zip + 4 Data: Opera Bureau

-------------------------

-------------------------

Account 1Account 2Account 3



23

Opportunity Areas

Overall, five areas of opportunity were identified by the Collections Diagnostic

Collector Performance

MaximizingPayments

Increasing Contact

AccountPrioritization

Reporting andAnalytics

Opera provided specific

recommendations for each area

A R E A S O FO P P O R T U N I T Y

24






Questions

Agenda

25

Collector Workstation powered by CRE

The Collector Dashboard, powered by the Collections Recommender Engine, provides detailed account level data and specific treatment recommendations for collectors instantly, allowing them to focus on converting contacts into payments

D E M O

C C P T R A I N I N G

M A N A G E R D A S H B O A R D : I N D I V I D U A L P E R F O R M A N C E

As of November 30, 2010P O R T F O L I O L O C A T I O N T E N U R E T E A M

T R E A T M E N T U S A G E

C U S T O M E R P R O F I L E S

CM SITUATION BALANCE RG PROBC BAND

C O N T A C T S

COMMENTARY

C C P

100

20

80

60

40

UPSET SICK UNEMPLOYED DISPUTE

CCP

AVERAGE

Lifetime YoY 1Y 6m 3m 1m

PROBC BAND

P T P %

COMMENTARYCM SITUATION


10

0-1

2030405060708090

100

1-3 3-5 5-10 10-25 25+

BALANCE RG PROBC BAND

A V E R A G E P A Y M E N T S I Z E

COMMENTARY

BALANCE RG

CM SITUATION


100

<1000 1000-2500

200

300

400

2500-5000 5000-7500 7500-9000 >9000

CM SITUATION BALANCE RG PROBC BAND

P T P K E P T %

COMMENTARY


10

UPSET

2030405060708090

100

SICK UNEMPLOYED DISPUTE

CCP is significantly below

average in contacting

hostile CM’s. Suggest

different approaches to

probing hostile CM’s

CCP is above average in

small balance ranges but

under performing in large

balance accounts.

Recommend second voice

while negotiating with all

accounts greater than

$25,000.

CCP is strong with CM’s

with low PROBC Scores

but needs to work on

negotiation skills with

good credit CM’s.

Recommend mandatory

second voice for all CM’s

with PROBC above 9000

CCP is significantly

underperform with

unemployed Cm’s. Review

talk-off strategies for

unemployed and hostile

customers. Recommend

having supervisor to assist

with unemployed CM’s for 2

weeks.

CCP

AVERAGE

CCP

AVERAGE

CCP

AVERAGE

Different filters allow leaders and managers to drill down and diagnose root-causes of

performance issues in real-time

P E R F O R M A N C E D A S H B O A R D

S C E N A R I O P L A N N I N G

M A N A G E R D A S H B O A R D : T R E N D R E V I E W

C U S T O M E R I D E N T I F I C A T I O N

P E R F O R M A N C E H E A T M A P

1 WEEK 2 WEEKS 1 MONTH 2 MONTHS 6MONTHS

TEAM 1 -2% -3% -5% -5% -8%

TEAM 2 -1% -3% -5% -5% -2%

TEAM 3 -2% -5% -2% -1% +1%

TEAM 4 +4% +1% +3% -3% -6%

TEAM 5 +1% +1% +1% +2% +5%

TEAM 6 0% +1% +1% +3% +4%

TEAM 7 +1% -2% +3% +1% +1%

TEAM 8 -1% -3% -1% -2% -2%

TEAM 9 -4% -5% -4% -5% -7%

TEAM 10 -6% -1% -3% -3% -3%

TEAM 11 +2% +3% +5% +4% +2%

TOTAL -2% -1% 0% -1% -1%

P O R T F O L I O L O C A T I O N T E N U R E T E A M C C P

P E R F O R M A N C E P E R I O D v s . P T P %

CONTACTS PER HOUR PTP % PTP KEPT % AVERAGE PAYMENT

T e a m

Positive and negative trends are identified

visually – leaders can drill down to identify root causes of the trends.

Dashboards are 100% customizable to capture site, team, and individual

performance trends.

As of November 30, 2010

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Questions

Agenda

29

The Opera Collections Insight CubeTM

I N S I G H T C U B E T M T E C H N O L O G Y

K E Y C H A R A C T E R I S T I C S

COMPREHENSIVE: Integrates all sources of collections data: disparate instances and systems (e.g., phone, dialer, agency and issuer data)

USER-FRIENDLY: Users access from personal desktop, using intuitive point-and-click, highly visual interface

FLEXIBLE: Features dynamic drill-down and reporting based on user’s area of interest

DETAILED: Drills down to account number level

ACTIONABLE: Enables rapid, complete identification of opportunities and specific, tactical actions

30

The Opera Collections Insight Cube™: Overview

Opera built an ‘Insight Cube’ using Radian’s default records; it can hold over 1 billion individual records and can be customized to cross-examine data quickly, efficiently, and without the use of complex queries

D E M O

31






Questions

Agenda

32

Questions?

Contact: Julian Romeu 646-644-3621

[email protected]

Documents

Leveraging Advanced Analytics to Improve Tax Collection Performance